Reviewerly/exHarmony huggingface dataset:
Optimizing the interval-centric distributed computing model for temporal graph algorithms

Temporal graphs assign lifespans to their vertices, edges and attributes. Large temporal graphs are common for finding the shortest paths in transit networks and contact tracing for COVID-19. Graph programming abstractions like Interval-centric Computing Model (ICM) extend Google's Pregel model to intuitively compose and execute time-dependent graph algorithms in a distributed environment. However, the benefits of easier algorithmic design in ICM are offset by performance bottlenecks in its TimeWarp shuffle and messaging phases. Here, we design several optimizations to ICM to reduce these overheads. We propose local optimizations within a vertex execution by unrolling messages before TimeWarp (LU), and deferring messaging till all local computations complete (DS). We also temporally partition the interval graph into windows (WICM) to flatten the execution load. We offer a proof of equivalence between ICM and these techniques. Our detailed empirical evaluation for six real-world graphs with up to 133M vertices, 5.5B edges and 365 time-points, for six temporal traversal algorithms executing on a commodity cluster with 8 nodes, shows that LU, DS and WICM together significantly reduce the average algorithm runtime by ≈ 61% (≈ 15 mins) over ICM, and reduce message communication by ≈ 38%(≈ 3.2B) on average.
pandas Library

In this chapter we introduce a new library called pandas which in turn offers the DataFrame data structure. The pandas library evolved from the use of arrays in Python’s NumPy package used in mathematical and scientific computing applications. The pandas library opens a new world of possibilities for data analysis. The main structure from the pandas library is the Series and DataFrame offering a structure to organize dissimilar types of data (strings, integers, and floats) into a single data structure and has the ability to easily apply methods or functions to all or portions of the data.
Multiple Watermarking for Images using Back-Propagation Neural Network and DWT

An effective multiple watermarking technique supported on neural network into the wavelet transform can be proposed. The wavelet coefficients has been preferred by Human Visual System. In the proposed work focus on Discrete Wavelet Transform based segmented image watermarking techniques using Back-Propagation neural networks. Using improved BPNN, the multiple watermarks are embedded into the original image, which can advance the pace of the learn, reduce the error and the qualified neural networks are extricate multiple watermarks as of the embedded images. The planned strategy achieves a excellent visual effect scheduled the watermarked images as well as high robustness on extracted multiple watermarks.
An image encryption scheme by applying memristive Hindmarsh-Rose neuron model

Abstract Neurophysiological studies indicate that there are rich and complex dynamical characteristics in electrical actives between neurons. When neurons are stimulated by appropriate electromagnetic, the dynamical behavior for the nervous system can be changed, and electromagnetic has an important effect for the effectiveness of neurological diseases. To explore the complex dynamic effect of magnetic field in neuron activities, a new memristive HindMarsh-Rose neuron model can be developed by connecting a memristor into a simple 2 dimentional HindMarsh-Rose neuron model. It fond that the new neuron model has rich nonlinear dynamics and generate hidden attractors when changing electromagnetic induction. Neuron model as a nonlinear system, it has properties similar to a chaotic system system. So the memristive HindMarsh-Rose neuron model can be considered as a chaotic system to apply in image encryption algorithm. And an image encryption algorithm based on the memristive HindMarsh-Rose neuron model is proposed in this paper. The security analysis confirmed that enryption algorithm is well.
Statemental credibility logic for intelligent systems

An indispensable function of intelligent systems is to perform logical reasoning in the presence of uncertainty. In this paper, we establish a mathematical framework, called statemental credibility logic (SCL), for inference under uncertainty. The proposed SCL consists of statemental algebra and truth calculus. The statemental algebra deals with the operations of statements which can be representations of deterministic, vague, random events, and their mixture. The truth calculus discusses the evaluation and inference of the truth values of dichotomy, fuzzy, probabilistic statements, and their combinations. We generalize the classical Bayesian networks and develop robust inference methods which have potential to construct more capable and reliable inference engines of intelligent systems.
Transformations and unresolved tensions

This chapter identifies some of the key unresolved tensions concerning digital technologies in school practices in Norway from the last two decades, and discusses implications of how students’ agency in using digital technologies is conceptualised in school curricula and relevant research projects. The key messages in this chapter are, first, that the field of digital technology in education has been defined by unresolved tensions that create polarisation rather than coherence in how we achieve progress and, second, that the potential for involving students and their agency in using digital technologies within schools is characterised by ambivalence.
Deep Learning-Based Model Significantly Improves Diagnostic Performance for Assessing Renal Histopathology in Lupus Glomerulonephritis

Assessment of glomerular lesions and structures plays an essential role in understanding the pathological diagnosis of glomerulonephritis and prognostic evaluation of many kidney diseases. Renal pathophysiological assessment requires novel high-throughput tools to conduct quantitative, unbiased, and reproducible analyses representing a central readout. Deep learning may be an effective tool for glomerulonephritis pathological analysis.We developed a murine renal pathological system (MRPS) model to objectify the pathological evaluation via the deep learning method on whole-slide image (WSI) segmentation and feature extraction. A convolutional neural network model was used for accurate segmentation of glomeruli and glomerular cells of periodic acid-Schiff-stained kidney tissue from healthy and lupus nephritis mice. To achieve a quantitative evaluation, we subsequently filtered five independent predictors as image biomarkers from all features and developed a formula for the scoring model.Perimeter, shape factor, minimum internal diameter, minimum caliper diameter, and number of objects were identified as independent predictors and were included in the establishment of the MRPS. The MRPS showed a positive correlation with renal score (r = 0.480, p < 0.001) and obtained great diagnostic performance in discriminating different score bands (Obuchowski index, 0.842 [95% confidence interval: 0.759, 0.925]), with an area under the curve of 0.78-0.98, sensitivity of 58-93%, specificity of 72-100%, and accuracy of 74-94%.Our MRPS for quantitative assessment of renal WSIs from MRL/lpr lupus nephritis mice enables accurate histopathological analyses with high reproducibility, which may serve as a useful tool for glomerulonephritis diagnosis and prognosis evaluation.
When They Go Low: Automated Replacement of Low-level Functions in Ethereum Smart Contracts

Smart contracts in the Ethereum blockchain are typically written using a high-level, Turing-complete language called Solidity. However, the Solidity language has many features to allow programmers fine-grained control over their smart contracts. We call these features low-level functions. Unfortunately, the improper use of low-level functions can lead to security vulnerabilities leading to heavy financial losses. Therefore, the Solidity community has suggested alternatives for the low-level functions in the official guidelines for developers. We first perform a large-scale empirical study on the use of low-level functions in Ethereum smart contracts written in Solidity. We find that such functions are widely used in real-world Ethereum smart contracts, and that the majority of these uses are gratuitous for the smart contract&#x0027;s functionality. We then propose GoHigh, a source-to-source transformation tool to eliminate low-level function-related vulnerabilities, by replacing low-level functions with high-level alternatives. We evaluate GoHigh on over 300,000 real-world smart contracts on the Ethereum blockchain. GoHigh replaces all low-level functions that are amenable to replacement in the contracts with 17&#x0025; fewer compiler warnings, and the externally-visible behaviors of at least 92 &#x0025; of the replaced contracts are identical to the original ones. Finally, GoHigh takes 7 seconds on average per contract.
Backdoors Against Natural Language Processing: A Review

Data poisoning attacks, specifically backdoor attacks, present a severe security threat in artificial intelligence. We provide a comprehensive survey into state-of-the-art backdoor attacks and defenses in the field of natural language processing.
Rise of the Automotive Health-Domain Controllers: Empowering Healthcare Services in Intelligent Vehicles

We are facing a global healthcare crisis today as the healthcare cost is ever climbing, but with the aging population, government fiscal revenue is ever dropping. To address this imminent problem, we can start by enabling affordable anywhere anytime healthcare access through delivering healthcare services on intelligent vehicles. The foundation upon which healthcare services can be provided on intelligent vehicles is an automotive health-domain controller (AHDC), which is missing today. In this article, for the first time, we explain the necessity and define the functionalities of AHDCs. In addition, we delve into the technical challenges, requirements, and feasibility of integrating multiple key features into AHDCs. It is hoped that this will help the community standardize on-vehicle healthcare services provisioning, and lead to the universal adoption of the revolutionary mobile healthcare system.
Automated Evaluation of Handwritten Answer Script Using Deep Learning Approach

Automatic Essay Grading (AEG) is one of the exciting research topics in the field of adopting technology in education. In the education system assessment of student’s answer script is a critical job of teachers; yet doing so consumes a significant amount of their time and prevents them from working on other tasks. In addition, evaluating a large number of exam scripts is error-prone, inefficient, and tedious. Natural Language Processing (NLP), has created such an opportunity to make the computer learn about written text data and make important decisions based on the learned model. Similarly, it is possible to make a computer be able to assess an answering script based on the model used to train our computer to learn about answers to predefined short questions. In this paper, we propose a deep learning architecture with a combination of Con- volutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) which has the ability to perform both handwritten answers recogni- tion and grading them as accurately as a human expert grader.
Efficient and Reliable Clustering by Parallel Random Swap Algorithm

Solving large-scale clustering problems requires an efficient algorithm which can be implemented also in parallel. K-means would be suitable but it can lead to an inaccurate clustering result. To overcome this problem, we present a parallel version of random swap clustering algorithm. It combines the scalability of k-means with high clustering accuracy. The new clustering method is experimented on top of Java parallel streams and lambda expressions, which offer interesting execution time benefits. The method is applied to standard benchmark datasets, with a varying population size and distribution of managed records, dimensionality of data points and the number of clusters. The experimental results confirm that high quality clustering can be obtained by parallel random swap together with a high time efficiency.
Flight Test of a Collision Avoidance Neural Network with Run-Time Assurance

Our team is developing assurance technologies that can support the use of machine learning in the design of safety-critical aircraft systems. These capabilities have been integrated on Boeing’s Autonomy Testbed Aircraft to show that they can provide evidence of correct operation and safety guarantees needed by real aircraft. We have applied run-time assurance along with formal methods synthesis, modeling, and analysis tools to an airborne collision avoidance system based on a neural network. This system was demonstrated in flight and shown to correctly monitor neural network operation and intervene when needed to prevent violation of the "remain well clear" safety requirement relative to an intruder aircraft.
A Hybrid Deep Learning Based Assist System for Detection and Classification of Breast Cancer from Mammogram Images

The most common cancer disease among all women is breast cancer. This type of disease is caused due to genetic mutation of ageing and lack of awareness. The tumour that occurred may be a benign type which is a non-dangerous and malignant type that is dangerous. The Mammography technique utilizes the early detection of breast cancer. A Novel Deep Learning technique that combines the deep convolutional neural networks and the random forest classifier is proposed to detect and categorize Breast cancer. The feature extraction is carried over by the AlexNet model of the Deep Convolutional Neural Network and the classifier precision is increased by Random Forest Classifier. The images are collected from the various Mammogram images of predefined datasets. The performance results confirm that the projected scheme has enhanced performance compared with the state-of-art schemes.
Transforming photon statistics through zero-photon subtraction

Zero-photon subtraction (ZPS) is a conditional measurement process that can reduce the mean photon number of quantum optical states without physically removing any photons. Here we show that ZPS can also be used to transform certain super-Poissonian states into sub-Poissonian states and vice versa. Combined with a well-known ``no-go'' theorem on conditional measurements, this effect leads to a set of nonclassicality criteria that can be experimentally tested through ZPS measurements.
A development of ubiquitous learning support system based on an enhanced inquiry-based learning approach

With the popularisation of broadband and wireless Internet technologies, researchers have attempted to develop mobile device-based learning systems to enable students to seamlessly learning in anywhere and anytime through internet network connections. However, it is difficult for teachers to provide the manner for asking students to share explanations with peers. To cope with this issue, this study proposes an enhanced inquiry-based learning approach for eliciting and sharing knowledge from students' investigation of a problem or task. Based on the proposed approach, a ubiquitous learning support system has been implemented in a web-programming course; moreover, an experiment was conducted on university students to evaluate the performance of our approach. By analysing the results from the two groups of students participating in the different systems, it was found that the proposed approach effectively enhanced the students' learning achievement and promoted positive perception toward the system. Furthermore, the proposed approach benefited both male and female students in the terms of learning achievement and perceptions.
The Implementation of Information and Communication Technology on Learning Process in Communication Department of UHO Facing Industrial Revolution 4.0 [Penerapan Teknologi Komunikasi dan Informasi Pada Pembelajaran Jurusan Ilmu Komunikasi UHO Menghadapi Revolusi Industri 4.0]

The most obvious progress in communication technology and information today is the speed and ease of access that can be enjoyed by anyone. Entering the era of industrial revolution 4.0, a collaboration between the world of education and communication technology should be encouraged to facilitate access to information in the learning process so that it can answer the challenges of today's modern world. Taking policy and decisions related to higher education must be able to adapt to the conditions in this fourth generation industrial revolution era. This paper tries to explain the urgency of a Communication and Information Technology Application Training in the Learning Process of the Communication Science Department in Facing the Age of Industrial Revolution 4.0. As for the approach used is an approach that we call a persuasive-educative approach, which is intended to provide a number of understandings, skills, and knowledge so that it can apply communication and information technology to the learning process of the communication science department. In this training, the methods were lectures, discussions and practices/exercises. The results show that through this Training can encourage the strengthening of academic programs and research and innovation in the learning process of communication sciences. The creation of a communication and information technology-based learning atmosphere in the communication science department that is supported by information systems and integrated education services in communication science laboratories.
The Formation of Creative Competency in the Process of Study оn the Basis of the Integrative Course “Choral Theater”

The relevance of the subject of the article is stipulated by the challenges of contemporary musical education in the preparation of professional cadres possessing creative competency. The elaboration of innovative integrational courses, which develop the given professional quality among future specialist musicians in the process of their studies at the institutions of higher education, acquires special significance. The aim of the research is to reveal the potentials of the integrative course “Choral Theater” in the formation of creative competency of the student choirmaster. Analysis of scholarly works has made it possible for the authors to define the concept of the “creative competency of the musician” as the ability to create the situation of creative success, to solve in a productive manner nonstandard professional goals, thereby achieving maximal effectivity. The article presents the results of experimental work which was carried out by the methods of narrative interviews, observation and expert evaluation, as well as the study of the attempt of realization of the integrative course “Choral Theater” in the cycle of special disciplines for conductors-choirmasters. The research demonstrates the high pedagogical potential of active interaction between students and faculty members in the work on the projects of this course. Obviously, joint activities develop skills of command work and stimulates to the utmost degree the participants’ creative activities. As an example of their project activities the authors highlight the performance of the play “Promise me Love…”. Keywords: competency, creative competency, choral theater, integrative course, professional education.
See No…, Hear No…, Track No…: Ethics and the Intelligent Campus

Many authors have expressed concerns about privacy in smart cities, where public spaces are continually monitored. Intelligent campuses – which feature an even more intricate web of public and private, living, working and social spaces – might seem to pose an even greater challenge. However campuses are more likely to be controlled by a single organisation, making it simpler to apply a consistent approach to the selection and design of their systems and controls. This paper examines the sensors likely to be deployed on an intelligent campus and assesses their risk using an extension of a framework approved by European regulators for Radio-Frequency Identification (RFID) systems. Two ethical frameworks – for Information &amp; Communications Technology (ICT) research and for public services – are applied to suggest how intelligent campus applications should be chosen, designed and reviewed in order to make them acceptable to their occupants. The barriers to delivering ‘smart citizenship’ are examined, concluding that intelligent campuses should find it easier to become truly ‘citizen-centric’. By moving the privacy focus from individual consent and fault-correction, after hazardous systems have been deployed, to empowering occupants in creating safer spaces through consensus, and making institutions accountable for delivering them, intelligent campuses can give confidence to both their managers and those who use them.
Innovation policy-making in the upstream oil and gas industry as a large technical system under economic transition

Purpose Innovation and technology development policy-making naturally encounters numerous uncertainties and complexities, especially in developing countries, for the sake of the prevailing prospect of decision makers focusing on hard evidences, and neglecting key and effective social ones; in this research, a context-based method by means of Q-methodology was designed to facilitate policy-making for complex systems by bridging between policy and practices (latent in viewpoints) through providing context-based evidences. Design/methodology/approach Due to the nature of knowledge-based systems, the performance of Innovation and Technology Development (ITD) systems is highly dependent on the standpoints of key players/stakeholders of the system. In consideration of Iran’s economy characteristics, Upstream Oil and Gas (UOG) Industry, which is one of the complex Large Technical Systems (LTS), was selected as a case study. Regarding the features of LTSs, the designed model was completed by adding hierarchical clustering method, as well as using the framework of innovation and technology learning transition model to analyze the results. Findings The results showed the capability of the model in providing credible evidences to inform policy-making processes. Originality/value This study is one of the first real experiences which used Q-method for providing evidence-based policy-making model in a complex Large Technical System, namely, Upstream Oil and Gas (UOG) Industry.
Sensing fabrication errors in diffraction gratings using high dynamic range imaging

We describe the design of a simple instrument for the identification and characterization of fabrication errors in diffraction gratings. The instrument uses an uncooled charge-coupled device (CCD) camera and a high dynamic range imaging process to detect the light scattered off a grating under test in the focal plane of a lens. We demonstrate that the instrument can achieve a dynamic range around nine orders of magnitude and we show that we are able to clearly identify small, periodic fabrication errors in two test gratings that could not be detected with microscopic techniques.
MixNet: An Energy-Scalable and Computationally Lightweight Deep Learning Accelerator

In this paper, we present a lightweight CNN model named MixNet which is easily scalable to different energy requirements in embedded platforms. The MixNet model uses two extreme bit-precisions that efficiently balances the model accuracy and energy consumption. The energy consumption in processing MixNet is managed by controlling the ratio between high-precision (16bit) and low-precision (1bit) paths. Since only two bit-precisions are required in designing a hardware accelerator, the control logic becomes simpler compared to other multi-precision accelerators. In addition, a reconfigurable multiplier is proposed to enable highly parallel MixNet computations for faster prediction and/or training. Overall, the energy efficiency in terms of run-time per unit power improves by 1.75 ~1.94 × over the recently proposed reduced-precision CNN model.